Improving Autonomous Exploration Using Reduced Approximated Generalized Voronoi Graphs

Autonomous robotic exploration has been extensively applied in many tasks, such as mobile mapping and indoor searching. One of the most challenging issues is to locate the Next-Best-View and to guide robots through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes an improving method based on reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. Additionally, a fast and robust image thinning algorithm for constructing RAGVGs from metric maps is presented, and an autonomous robotic exploration framework using RAGVGs is designed. The proposed method is validated with three known common data sets and two simulations of autonomous exploration tasks. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and the simulations indicate that the mobile robot controlled by the RAGVG-based exploration method reduced the total time by approximately 20% for the given tasks.

[1]  Antonio Adán,et al.  Semantic scan planning for indoor structural elements of buildings , 2016, Adv. Eng. Informatics.

[2]  Howie Choset,et al.  Principles of Robot Motion: Theory, Algorithms, and Implementations [Book Review] , 2005, IEEE Robotics & Automation Magazine.

[3]  Emmanouil Tsardoulias,et al.  Construction of Minimized Topological Graphs on Occupancy Grid Maps Based on GVD and Sensor Coverage Information , 2014, J. Intell. Robotic Syst..

[4]  Wolfram Burgard,et al.  Improved updating of Euclidean distance maps and Voronoi diagrams , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Nicola Basilico,et al.  Exploration strategies based on multi-criteria decision making for searching environments in rescue operations , 2011, Auton. Robots.

[6]  Shayok Mukhopadhyay,et al.  Autonomous robotic exploration based on multiple rapidly-exploring randomized trees , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[7]  Howie Choset,et al.  Sensor Based Planing, Part I: The Generalized Voronoi Graph , 1995, ICRA.

[8]  Arnoud Visser,et al.  Beyond Frontier Exploration , 2008, RoboCup.

[9]  Khalid Saeed,et al.  Implementation and Advanced Results on the Non-interrupted Skeletonization Algorithm , 2001, CAIP.

[10]  A. Lastra,et al.  An Adaptive Hierarchical Next-Best-View Algorithm for 3D Reconstruction of Indoor Scenes , 2006 .

[11]  J. Hershberger,et al.  Speeding Up the Douglas-Peucker Line-Simplification Algorithm , 1992 .

[12]  Wolfram Burgard,et al.  Mapping and exploration with mobile robots using coverage maps , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[13]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[14]  Emmanouil Tsardoulias,et al.  Cost-Based Target Selection Techniques Towards Full Space Exploration and Coverage for USAR Applications in a Priori Unknown Environments , 2017, J. Intell. Robotic Syst..

[15]  Vijay Kumar,et al.  Autonomous robotic exploration using a utility function based on Rényi’s general theory of entropy , 2018, Auton. Robots.

[16]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[17]  Libor Preucil,et al.  An Integrated Approach to Goal Selection in Mobile Robot Exploration , 2019, Sensors.

[18]  H. Choset,et al.  Toward robust sensor based exploration by constructing reduced generalized Voronoi graph , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[19]  Vijay Kumar,et al.  Autonomous robotic exploration using occupancy grid maps and graph SLAM based on Shannon and Rényi Entropy , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Emmanouil Tsardoulias,et al.  A Review of Global Path Planning Methods for Occupancy Grid Maps Regardless of Obstacle Density , 2016, J. Intell. Robotic Syst..

[21]  Ching Y. Suen,et al.  A fast parallel algorithm for thinning digital patterns , 1984, CACM.

[22]  Osamu Takahashi,et al.  Motion planning in a plane using generalized Voronoi diagrams , 1989, IEEE Trans. Robotics Autom..

[23]  D. Calisi,et al.  Autonomous navigation and exploration in a rescue environment , 2005, IEEE International Safety, Security and Rescue Rototics, Workshop, 2005..

[24]  Wolfram Burgard,et al.  Information Gain-based Exploration Using Rao-Blackwellized Particle Filters , 2005, Robotics: Science and Systems.

[25]  Juan Andrade-Cetto,et al.  Potential information fields for mobile robot exploration , 2015, Robotics Auton. Syst..

[26]  Wolfram Burgard,et al.  Exploration with active loop-closing for FastSLAM , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[27]  Steven M. LaValle,et al.  Resolution complete rapidly-exploring random trees , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[28]  Khalid Saeed,et al.  K3M: A universal algorithm for image skeletonization and a review of thinning techniques , 2010, Int. J. Appl. Math. Comput. Sci..

[29]  Wolfram Burgard,et al.  Coordinated multi-robot exploration , 2005, IEEE Transactions on Robotics.

[30]  Magnus Egerstedt,et al.  A provably complete exploration strategy by constructing Voronoi diagrams , 2010, Auton. Robots.

[31]  Héctor H. González-Baños,et al.  Navigation Strategies for Exploring Indoor Environments , 2002, Int. J. Robotics Res..

[32]  Howie Choset,et al.  Sensor-Based Exploration: The Hierarchical Generalized Voronoi Graph , 2000, Int. J. Robotics Res..

[33]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[34]  Luis Moreno,et al.  The Path to Efficiency: Fast Marching Method for Safer, More Efficient Mobile Robot Trajectories , 2013, IEEE Robotics & Automation Magazine.